Hiroyuki Narihisa

Software Reliability Growth Models with Testing-Effort

Many software reliability growth models have been proposed in the past decade. Those models tacitly assume that testing-effort expenditures are constant throughout software testing. This paper develops realistic software reliability growth models incorporating the effect of testing-effort. The software error detection phenomenon in software testing is modeled by a nonhomogeneous Poisson process. The software reliability assessment measures and the estimation methods of parameters are investigated. Testing-effort expenditures are described by exponential and Rayleigh curves. Least-squares estimators and maximum likelihood estimators are used for the reliability growth parameters. The software reliability data analyses use actual data. The software reliability growth models with testing-effort can consider the relationship between the software reliability growth and the effect of testing-effort. Thus, the proposed models will enable us to evaluate software reliability more realistically.

Performance of simulated annealing-based heuristic for the unconstrained binary quadratic programming problem

Abstract The unconstrained binary quadratic programming problem (BQP) is known to be NP-hard and has many practical applications. This paper presents a simulated annealing (SA)-based heuristic for the BQP. The new SA heuristic for the BQP is based on a simple (1- opt ) local search heuristic and designed with a simple cooling schedule, but the multiple annealing processes are adopted. To show practical performances of the SA, we test on publicly available benchmark instances of large size ranging from 500 to 2500 variables and compare them with other heuristics such as multi-start local search, the previous SA, tabu search, and genetic algorithm incorporating the 1- opt local search. Computational results indicate that our SA leads to high-quality solutions with short times and is more effective than the competitors particularly for the largest benchmark set. Furthermore, the values of new best-known solutions found by the SA for several large instances are also reported.

An effective local search for the maximum clique problem

We propose a variable depth search based algorithm, called k-opt local search (KLS), for the maximum clique problem. KLS efficiently explores the k-opt neighborhood defined as the set of neighbors that can be obtained by a sequence of several add and drop moves that are adaptively changed in the feasible search space. Computational results on DIMACS benchmark graphs indicate that KLS is capable of finding considerably satisfactory cliques with reasonable running times in comparison with those of state-of-the-art metaheuristics.

Optimum release policies for a software system with a scheduled software delivery time

Optimum release policies minimizing the total expected software cost with a scheduled software delivery time are discussed. Such cost considerations enable us to make a release decision as to when to transfer a software system from testing phase to operational phase. The underlying reliability model describing a software error occurrence phenomenon is a software reliability growth model based on a non-homogeneous Poisson process. It is assumed that the penalty cost functions due to delay for a scheduled software delivery time are proportional and exponential to time, respectively. We consider two cases; when the scheduled software delivery time is a constant and when it is a random variable with an arbitrary distribution. Four useful theorems are derived to determine optimum release times of a software system for operational use. Numerical examples are shown to illustrate the results.

The efficiency of hybrid mutation genetic algorithm for the travelling salesman problem

In this paper, we present an efficient genetic algorithm (GA) for solving the travelling salesman problem (TSP) as a combinatorial optimization problem. In our computational model, we propose a complete subtour exchange crossover that does not break as some good subtours as possible, because the good subtours are worth preserving for descendants. Generally speaking, global search GA is considered to be better approaches than local searches. However, it is necessary to strengthen the ability of local search as well as global ones in order to increase a GA total efficiency. In this study, our GA applies a stochastic hill climbing procedure in the mutation process of the GA. Experimental results showed that the GA leads good convergence as high as 99 percent even for 500 cities TSP.

Optimizing a Multiple Classifier System

Recently, multiple classifier systems (MCS) have been used for practical applications to improve classification accuracy. Self-generating neural networks (SGNN) are one of the suitable base-classifiers for MCS because of their simple setting and fast learning. However, the computation cost of the MCS increases in proportion to the number of SGNN. In this paper, we propose a novel optimization method for the structure of the SGNN in the MCS. We compare the optimized MCS with two sampling methods. Experiments have been conducted to compare the optimized MCS with an unoptimized MCS, the MCS based on C4.5, and k-nearest neighbor. The results show that the optimized MCS can improve its classification accuracy as well as reducing the computation cost.

Improving Generalization Ability of Self-Generating Neural Networks Through Ensemble Averaging

We present an ensemble averaging effect for improving the generalization capability of self-generating neural networks applied to classification problems. The results of our computational experiments show that ensemble averaging effect is 1-7% improvements in accuracy comparing with single SGNN for three benchmark problems.

A testing-effort dependent software reliability model and its application

Abstract We discuss a software reliability growth model with testing-effort based on a nonhomogeneous Poisson process and its application to a testing-effort control problem. The time-dependent behaviour of testing-effort expenditures which is incorporated into software reliability growth is expressed by a Weibull curve due to the flexibility in describing a number of testing-effort expenditure patterns. Using several sets of actual software error data, the model fitting and examples of a testing-effort control problem are illustrated.

A new iterated local search algorithm using genetic crossover for the traveling salesman problem

This paper proposes a new iterared local search (ILS) algorit.hm ihar escapes from local optima usin, a geuet ic crossover. In usual IL9 for solving the rraveling salesman problem, a double-bridge 4change move is geuerally employed as a useful technique to escape from t.he local opt ima fouud by a local search procedure. Proposed ILS uses a technique of crossover developed in a field of the genetic algorit.hms in spite of the double-bridge move. In our algorithm, !ve emplo\the disrauce preserviug crossover (UPX) proposed by Freislebeu and Merz. Therefore rhis DPS is performed as a special k-change ulove according to srates of t.wo solutions Lbat ueed fol crossover process. Experimeutal results demoust.rate t.hat proposed ILS Buds much better quality solutions than usual ILS using the double-bridge move. (:ousequeut.ly. this paper will show au efrect to employ tbe genet.ic crossover as the escape t.echuique.

Solving the maximum clique problem by k-opt local search

This paper presents a local search algorithm based on variable depth search, called the k-opt local search, for the maximum clique problem. The k-opt local search performs add and drop moves, each of which can be interpreted as 1-opt move, to search a k-opt neighborhood solution at each iteration until no better k-opt neighborhood solution can be found. To evaluate our k-opt local search algorithm, we repeatedly apply the local search for each of DIMACS benchmark graphs and compare with the state-of-the-art metaheuristics such as the genetic local search and the iterated local search reported previously. The computational results show that in spite of the absence of major metaheuristic components, the k-opt local search is capable of finding better (at least the same) solutions on average than those obtained by these metaheuristics for all the graphs.